def test_basic(cloud_texture_cube):
"""Test output type and metadata"""
expected_dims = ["projection_y_coordinate", "projection_x_coordinate"]
expected_aux = {
coord.name()
for coord in cloud_texture_cube.coords(dim_coords=False)
}
expected_attributes = {
"institution": "unknown",
"source": "IMPROVER",
"title": "unknown",
}
cubes = [cloud_texture_cube]
result = ShowerCondition()(CubeList(cubes))
dim_coords = [coord.name() for coord in result.coords(dim_coords=True)]
aux_coords = {coord.name() for coord in result.coords(dim_coords=False)}
assert result.name() == "precipitation_is_showery"
assert result.units == "1"
assert result.shape == (3, 3)
assert result.data.dtype == FLOAT_DTYPE
assert aux_coords == expected_aux
assert dim_coords == expected_dims
assert result.attributes == expected_attributes
def process(*cubes: cli.inputcube):
"""
Determine the shower condition from global or UK data depending on input fields
Args:
cubes (iris.cube.CubeList):
List of cubes to input into shower diagnosis. These should
contain probabilities of EITHER cloud texture from a high-resolution model,
OR cloud area fraction and convective ratio from a low-resolution model.
Returns:
iris.cube.Cube:
Binary (0/1) "precipitation is showery"
"""
from iris.cube import CubeList
from improver.precipitation_type.shower_condition import ShowerCondition
return ShowerCondition()(CubeList(cubes))
def test_missing_threshold(cloud_texture_cube):
"""Test error if the required threshold is missing"""
cubes = [cloud_texture_cube[0]]
with pytest.raises(ValueError, match="contain required threshold"):
ShowerCondition()(CubeList(cubes))
def test_too_few_inputs(cloud_cube):
"""Test error if too few inputs are provided"""
cubes = [cloud_cube]
with pytest.raises(ValueError, match="Incomplete inputs"):
ShowerCondition()(CubeList(cubes))
def test_too_many_inputs(cloud_texture_cube, cloud_cube, conv_ratio_cube):
"""Test default behaviour using UK tree if all fields are provided"""
cubes = [cloud_texture_cube, cloud_cube, conv_ratio_cube]
result = ShowerCondition()(CubeList(cubes))
np.testing.assert_allclose(result.data, EXPECTED_UK)
def test_global_tree(cloud_cube, conv_ratio_cube):
"""Test correct shower diagnosis using global decision tree"""
cubes = [cloud_cube, conv_ratio_cube]
result = ShowerCondition()(CubeList(cubes))
np.testing.assert_allclose(result.data, EXPECTED_GLOBAL)
def test_uk_tree(cloud_texture_cube):
"""Test correct shower diagnosis using UK decision tree"""
cubes = [cloud_texture_cube]
result = ShowerCondition()(CubeList(cubes))
np.testing.assert_allclose(result.data, EXPECTED_UK)